Flow Monitoring Method Based on Quantitative Recursive Feature Extraction
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摘要: 提出一种基于定量递归特征提取的流量预测算法,构建了网络端到端路由缓冲区短时网络流量的时间序列分析模型.采用虚假最近邻点算法和平均互信息算法对网络流量时间序列进行相空间重构,计算递归图平面中时频特征点占平面总点数的百分比,实现网络流量的时频熵特征提取,有效反应流量时间序列的内部结构特征和变化趋势,实现对流量的准确预测和监测.仿真结果表明,采用该算法能准确实现对网络流量相轨迹的预测判断,预测过程具有较好的抗干扰能力,预测精度较高.Abstract: A new time series analysis model based on quantitative recursive feature extraction was proposed, the time series analysis model of network traffic time series was constructed. The false nearest neighbor algorithm and the average mutual information algorithm were used to reconstruct the phase space of the time series of network traffic.The percent of the frequency characteristic points of the total points of the plane was calculated in the recursive figure plane, and the feature of network traffic time-frequency entropy was extracted, which can effectively reflect the internal structure of flow time series characteristics and change tendency and achieve the accurate prediction and monitoring of flow. Simulation results showed that the proposed algorithm can accurately predict the trajectory of the network traffic, and has good anti-interference ability and higher prediction accuracy.
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[1] 杨雷,李贵鹏,张萍.改进的 Wolf 一步预测的网络异常流量检测[J].科技通报,2014,30(2):47-49. [2] 李振刚.基于高斯过程回归的网络流量预测模型[J].计算机应用,2014,34(5):1251-1254. -
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